Submission¶

Put the ipynb file and html file in the github branch you created in the last assignment and submit the link to the commit in brightspace

In [1]:
from plotly.offline import init_notebook_mode
import plotly.io as pio
import plotly.express as px

init_notebook_mode(connected=True)
pio.renderers.default = "plotly_mimetype+notebook"
In [2]:
#load data
df = px.data.gapminder()
df.head()
Out[2]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.801 8425333 779.445314 AFG 4
1 Afghanistan Asia 1957 30.332 9240934 820.853030 AFG 4
2 Afghanistan Asia 1962 31.997 10267083 853.100710 AFG 4
3 Afghanistan Asia 1967 34.020 11537966 836.197138 AFG 4
4 Afghanistan Asia 1972 36.088 13079460 739.981106 AFG 4

Question 1:¶

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

  • Extract the 2007 year data from the dataframe. You have to process the data accordingly
  • use plotly bar
  • Add different colors for different continents
  • Sort the order of the continent for the visualisation. Use axis layout setting
  • Add text to each bar that represents the population
In [3]:
# I did question 1, 2 and 3 at once, as said in the lecture

df_2007 = df.query('year==2007')
df_2007_group = df_2007.groupby('continent').sum()
fig = px.bar(df_2007_group, x="pop", y=df_2007_group.index, color=df_2007_group.index, text_auto='.2s')
fig.update_layout(yaxis={'categoryorder': 'total ascending'}, showlegend=False)
fig.show()

Question 2:¶

Sort the order of the continent for the visualisation

Hint: Use axis layout setting

In [4]:
# I did question 1, 2 and 3 at once, as said in the lecture

fig = px.bar(df_2007_group, x="pop", y=df_2007_group.index, color=df_2007_group.index, text_auto='.2s')
fig.update_layout(yaxis={'categoryorder': 'total ascending'}, showlegend=False)
fig.show()

Question 3:¶

Add text to each bar that represents the population

In [5]:
# I did question 1, 2 and 3 at once, as said in the lecture

fig = px.bar(df_2007_group, x="pop", y=df_2007_group.index, color=df_2007_group.index, text_auto='.2s')
fig.update_layout(yaxis={'categoryorder': 'total ascending'}, showlegend=False)
fig.show()

Question 4:¶

Thus far we looked at data from one year (2007). Lets create an animation to see the population growth of the continents through the years

In [6]:
df_continent = df.groupby(['continent', 'year']).sum().reset_index()
fig = px.bar(df_continent, x="pop", y='continent', 
             animation_frame="year", animation_group='continent', 
             hover_name='continent', range_x=[0,4000000000],
             color='continent', text_auto='.2s')
fig.update_layout(yaxis={'categoryorder': 'total ascending'}, showlegend=False)
fig.show()

Question 5:¶

Instead of the continents, lets look at individual countries. Create an animation that shows the population growth of the countries through the years

In [7]:
df_country = df.groupby(['country', 'year']).sum().reset_index()
fig = px.bar(df_country, x='pop', y='country', color='country', 
             animation_frame='year', animation_group='country', 
             hover_name='country', range_x=[0,1500000000])
fig.update_layout(yaxis={'categoryorder': 'total ascending'}, showlegend=False)
fig.show()

Question 6:¶

Clean up the country animation. Set the height size of the figure to 1000 to have a better view of the animation

In [8]:
fig = px.bar(df_country, x='pop', y='country', 
             animation_frame='year', animation_group='country', 
             hover_name='country', range_x=[0,1500000000],
             color='country', height=1000)
fig.update_layout(yaxis={'categoryorder': 'total ascending'}, showlegend=False)

fig.show()

Question 7:¶

Show only the top 10 countries in the animation

Hint: Use the axis limit to set this.

In [9]:
fig = px.bar(df_country, x='pop', y='country', 
             animation_frame='year', animation_group='country', 
             hover_name='country', color='country', range_x=[0,1500000000],
             range_y=[len(df_country['country'].unique())-10.5, len(df_country['country'].unique())-0.5]) 
                                                            #0.5 is to scale the bars, so we don't get half bars in the plot
fig.update_layout(yaxis={'categoryorder': 'total ascending'}, showlegend=False)

fig.show()
In [ ]: